| A directed acyclic graph network combined with CNN and LSTM for remaining useful life prediction J Li, X Li, D He IEEE Access 7, 75464-75475, 2019 | 268 | 2019 |
| Semi-supervised gear fault diagnosis using raw vibration signal based on deep learning LI Xueyi, LI Jialin, QU Yongzhi, HE David Chinese Journal of Aeronautics 33 (2), 418-426, 2020 | 118 | 2020 |
| Gear pitting fault diagnosis using integrated CNN and GRU network with both vibration and acoustic emission signals X Li, J Li, Y Qu, D He Applied Sciences 9 (4), 768, 2019 | 102 | 2019 |
| Gear pitting fault diagnosis with mixed operating conditions based on adaptive 1D separable convolution with residual connection X Li, J Li, C Zhao, Y Qu, D He Mechanical Systems and Signal Processing 142, 106740, 2020 | 96 | 2020 |
| Unsupervised rotating machinery fault diagnosis method based on integrated SAE–DBN and a binary processor J Li, X Li, D He, Y Qu Journal of Intelligent Manufacturing 31, 1899-1916, 2020 | 62 | 2020 |
| A domain adaptation model for early gear pitting fault diagnosis based on deep transfer learning network J Li, X Li, D He, Y Qu Proceedings of the Institution of Mechanical Engineers, Part O: Journal of …, 2020 | 43 | 2020 |
| A Bayesian optimization AdaBN-DCNN method with self-optimized structure and hyperparameters for domain adaptation remaining useful life prediction J Li, D He Ieee Access 8, 41482-41501, 2020 | 41 | 2020 |
| A novel method for early gear pitting fault diagnosis using stacked SAE and GBRBM J Li, X Li, D He, Y Qu Sensors 19 (4), 758, 2019 | 40 | 2019 |
| Domain adaptation remaining useful life prediction method based on AdaBN-DCNN J Li, X Li, D He 2019 Prognostics and System Health Management Conference (PHM-Qingdao), 1-6, 2019 | 35 | 2019 |
| Graph neural network architecture search for rotating machinery fault diagnosis based on reinforcement learning J Li, X Cao, R Chen, X Zhang, X Huang, Y Qu Mechanical Systems and Signal Processing 202, 110701, 2023 | 31 | 2023 |
| A sequence-to-sequence remaining useful life prediction method combining unsupervised LSTM encoding-decoding and temporal convolutional network J Li, R Chen, X Huang Measurement Science and Technology 33 (8), 085013, 2022 | 15 | 2022 |
| Development of deep residual neural networks for gear pitting fault diagnosis using Bayesian optimization J Li, R Chen, X Huang, Y Qu IEEE Transactions on Instrumentation and Measurement 71, 1-15, 2022 | 8 | 2022 |
| Prediction of remaining fatigue life of metal specimens using data-driven method based on acoustic emission signal J Li, X Cao, R Chen, C Zhao, Y Li, X Huang Applied Acoustics 211, 109571, 2023 | 6 | 2023 |
| PSO optimized ANN diagnosis of early gear pitting J Li, Y Qu, L Hong, D He 2018 Prognostics and System Health Management Conference (PHM-Chongqing …, 2018 | 2 | 2018 |
| GEAR PITTING FAULT DIAGNOSIS USING RAW ACOUSTIC EMISSION SIGNAL BASED ON DEEP LEARNING. LI Xueyi, LI Jialin, HE David, QU Yongzhi Maintenance & Reliability/Eksploatacja i Niezawodność 21 (3), 2019 | | 2019 |
